Over the last few decades, Unmanned Aircraft Systems, or UAS, have become a critical part of the defense of our nation and the growth of the aerospace sector. UAS have already demonstrated a positive impact in many industries such as agriculture, first response, and ecological monitoring. Recently, there has been an increasing push industry-wide for UAS platforms to perform novel tasks such as Short Take-Off and Landing, or STOL, deep stall landings, or other acrobatic maneuvers. Of course, these novel tasks cannot be completed solely with innovative vehicle design, rather a more holistic approach is required. The ability to develop novel control systems that can perform such tasks is highly limited by the computational abilities of the autopilot system on board the UAS. In general, commercial-off-the-shelf (COTS) autopilots are split between between two categories: open-source autopilots and closed-source autopilots. The latter feature low-quality hardware and unreliable software, but a low price point; whereas, the former are extremely reliable, but highly proprietary, relatively expensive, and limited in their capability to perform novel tasks. These limitations clearly restrict the ability for researchers to push the boundaries of higher functionality for UAS.
The wide range of applications of UAS mentioned above has resulted in countless mission specific Unmanned Aerospace Vehicle, or UAV, platforms. These platforms must operate reliably in a range of environments, and in presence of significant uncertainties. The accepted practice for enabling autonomously flying UAVs today relies on extensive manual tuning of the UAV autopilot parameters or time consuming approximate modeling of the dynamics of the UAV. In practice, these methods usually lead to overly conservative controllers or excessive development times. Furthermore, controllers cannot be simply transferred from one UAV to another, rather each platform must be tuned independently of the others in order to achieve the desired performance criteria. This process can be extremely costly and time consuming for companies.
To solve these problems, this thesis posits the use of adaptive control to provide an airframe-independent control algorithm. The problem is framed using past works in adaptive control and Rapid Controller Transfer (RCT). However, RCT has not been realized on fixed wing UAV platforms in the outdoor environment. The primary goal of RCT is to transfer autopilot hardware with negligible effects on the controller performance from a source system, whose dynamics are well-known, to a transfer system, whose dynamics are poorly understood. A practical example of this could be transferring an autopilot from an Aerosonde airframe, the well-known source system, to a Zaggi airframe, the unknown transfer system.
Before proceeding to a description of the present invention, however, it should be noted and remembered that the description of the invention which follows, together with the accompanying drawings, should not be construed as limiting the invention to the examples (or embodiments) shown and described. This is so because those skilled in the art to which the invention pertains will be able to devise other forms of this invention within the ambit of the appended claims.